A targeted strategic peer support intervention to increase adherence to video teletherapy exposure and response prevention treatment for obsessive-compulsive disorder: a retrospective observational analysis
Bibliographic record
Abstract
Exposure and response prevention (ERP) therapy, a form of cognitive-behavioral therapy, is a first-line, evidence-based treatment for obsessive-compulsive disorder (OCD) for adults and children. It is effective for the majority of those who engage in it, but treatment adherence can be challenging for some due to the stress involved in the treatment as well as different life circumstances that arise. To help improve treatment adherence, NOCD, a provider of video teletherapy ERP, identifies those at risk of non-adherence using a prediction algorithm trained on a data set of N = 13,809 and provides targeted peer support interventions by individuals (“Member Advocates”) who successfully completed ERP treatment for OCD. Member Advocates, using lived OCD experience as well as experience with ERP, engage at-risk patients through digital messaging to engage, educate, and encourage patients in the early stages of treatment. From June 2022 to August 2022, N = 815 patients deemed at risk were reached out to and n = 251 responded and engaged with the Member Advocates. In the at-risk patients who engaged, the intervention resulted in a significant mean 30.4% more therapy hours completed compared to those who did not engage. Additionally, engaged patients had greater reductions in OCD severity. These results have implications for how data science, digital interventions, and strategic peer-to-peer communication and support can be combined to enhance the effectiveness of treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".